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On the inaccuracy of numerical ratings: dealing with biased opinions in social networks

Author

Listed:
  • Roberto Centeno

    (Dpto. de Lenguajes y Sistemas Informáticos, UNED)

  • Ramón Hermoso

    (University of Essex)

  • Maria Fasli

    (University of Essex)

Abstract

In this work, we study the potential problems emanating from using numerical ratings in social networks to rank entities regarding their reputation. In particular, we empirically demonstrate how reputation rankings as collected and managed by current systems are likely to be skewed due to subjectivity problems associated with the use of numerical ratings to encapsulate preferences. With the aim of overcoming these problems, we put forward an approach in which users are asked for their opinions about entities in a comparative fashion. In order to select the most appropriate users to be queried, we take advantage of the social structure derived from the interactions among users and entities following a principle of heterogeneity. Finally, we evaluate the proposed approach in the domain of movie ratings by using real datasets collected from different web sites.

Suggested Citation

  • Roberto Centeno & Ramón Hermoso & Maria Fasli, 2015. "On the inaccuracy of numerical ratings: dealing with biased opinions in social networks," Information Systems Frontiers, Springer, vol. 17(4), pages 809-825, August.
    • Handle: RePEc:spr:infosf:v:17:y:2015:i:4:d:10.1007_s10796-014-9526-1
    DOI: 10.1007/s10796-014-9526-1
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    References listed on IDEAS

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    1. Michael Anderson & Jeremy Magruder, 2012. "Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database," Economic Journal, Royal Economic Society, vol. 122(563), pages 957-989, September.
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    Cited by:

    1. Kawaljeet Kaur Kapoor & Kuttimani Tamilmani & Nripendra P. Rana & Pushp Patil & Yogesh K. Dwivedi & Sridhar Nerur, 2018. "Advances in Social Media Research: Past, Present and Future," Information Systems Frontiers, Springer, vol. 20(3), pages 531-558, June.
    2. Gao, Jian & Zhou, Tao, 2017. "Evaluating user reputation in online rating systems via an iterative group-based ranking method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 546-560.
    3. Young-Jin Lee & Kellie B. Keeling & Andrew Urbaczewski, 2019. "The Economic Value of Online User Reviews with Ad Spending on Movie Box-Office Sales," Information Systems Frontiers, Springer, vol. 21(4), pages 829-844, August.
    4. Carlos Iván Chesñevar & Eva Onaindia & Sascha Ossowski & George Vouros, 2015. "Special issue on agreement technologies," Information Systems Frontiers, Springer, vol. 17(4), pages 707-711, August.
    5. Matteo Manca & Ludovico Boratto & Salvatore Carta, 2018. "Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system," Information Systems Frontiers, Springer, vol. 20(4), pages 825-839, August.
    6. Matteo Manca & Ludovico Boratto & Salvatore Carta, 0. "Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system," Information Systems Frontiers, Springer, vol. 0, pages 1-15.

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